Sun.Dec 17, 2023

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Analytics in Machining Techniques in Making Advanced Exoskeleton Robots

Smart Data Collective

Analytics technology has seriously disrupted the manufacturing industry over the last decade. According to Mordor Intelligence, the market for analytics in manufacturing will be worth $19.5 billion by 2028. There are a number of ways that analytics has helped manufacturing companies improve their bottom line.

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Data-Driven Decision Making: Leveraging Analytics in Process Management

Modern Analyst

The function of business analysts has changed dramatically in today's technologically-advancing, digitally transformed business environment. Using analytics for data-driven decision-making is one of the major areas where their experience is becoming more and more important, particularly in the field of process management. It clarifies how business analysts can use data to optimize workflows and lead organizations toward long-term success.

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Maximizing Project Efficiency With A Point Cloud Viewer Software

Smart Data Collective

Cloud technology has been a godsend for companies in almost every sector. Therefore, it should not be particularly surprising that a growing number of companies are investing in cloud-based tools to improve scalability and boost profits.

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Tailored financial dashboards for actionable insights

Phocas

A business planning and analytics platform allows finance teams to integrate multiple sets of data sources together to create financial statements, budgets and forecasts.

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Individual Development Plan Template

There’s no way around it: sometimes employees need your help. Use this free template to clarify expectations, share resources, and set a timeline. Best case scenario, this process helps them improve. Worst case, your great recordkeeping keeps you compliant even if they move on. Download the template today!

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Artificial Intelligence and Machine Learning in Software Development

Smart Data Collective

New technology has always transformed aspects of our lives, but perhaps none has more potential to bring change than artificial intelligence (AI) and machine learning (ML). While the latest developments are watched with excitement by some and trepidation by others, engineers must look at how best to use them.